arielreplicate/scalable_diffusion_with_transformers:089c3506

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arielreplicate /scalable_diffusion_with_transformers:089c3506

Playground API

Input

DiT_resolution

string

Output image size

Default: "256x256"

VAE_Decoder

string

decoder type

Default: "sd-vae-ft-mse"

num_sampling_steps

integer

(minimum: 0, maximum: 1000)

Number of denoising steps

Default: 250

cfg_scale

number

(minimum: 1, maximum: 10)

Number of denoising steps

Default: 4

class_name

string

Which ImageNet class to generate

Default: "centipede"

num_outputs

integer

How many outputs

Default: 4

Run this model in Node.js with one line of code:

npx create-replicate --model=arielreplicate/scalable_diffusion_with_transformers

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85",
  {
    input: {
      cfg_scale: 4,
      class_name: "centipede",
      VAE_Decoder: "sd-vae-ft-mse",
      num_outputs: 4,
      DiT_resolution: "256x256",
      num_sampling_steps: 250
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85",
    input={
        "cfg_scale": 4,
        "class_name": "centipede",
        "VAE_Decoder": "sd-vae-ft-mse",
        "num_outputs": 4,
        "DiT_resolution": "256x256",
        "num_sampling_steps": 250
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85",
    "input": {
      "cfg_scale": 4,
      "class_name": "centipede",
      "VAE_Decoder": "sd-vae-ft-mse",
      "num_outputs": 4,
      "DiT_resolution": "256x256",
      "num_sampling_steps": 250
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/arielreplicate/scalable_diffusion_with_transformers@sha256:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85 \
  -i 'cfg_scale=4' \
  -i 'class_name="centipede"' \
  -i 'VAE_Decoder="sd-vae-ft-mse"' \
  -i 'num_outputs=4' \
  -i 'DiT_resolution="256x256"' \
  -i 'num_sampling_steps=250'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/arielreplicate/scalable_diffusion_with_transformers@sha256:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "cfg_scale": 4,
      "class_name": "centipede",
      "VAE_Decoder": "sd-vae-ft-mse",
      "num_outputs": 4,
      "DiT_resolution": "256x256",
      "num_sampling_steps": 250
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

No output yet! Press "Submit" to start a prediction.